LLMs believe false statements even after explicit warnings that they're false

Researchers found that large language models (LLMs) continue to internalize and rely on false information even when explicitly warned that the statements are untrue. The study highlights a fundamental limitation in current AI reasoning, where warnings alone are insufficient to override ingrained training data biases. This raises concerns about the reliability of LLMs in factual tasks.